Structural Engineering and Mechanics

  • 제34권5호
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  • Pages.625-634
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  • 2010
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

New accuracy indicator to quantify the true and false modes for eigensystem realization algorithm

  • Wang, Shuqing (College of Engineering, Ocean University of China) ;
  • Liu, Fushun (College of Engineering, Ocean University of China)
  • 투고 : 2008.12.17
  • 심사 : 2009.12.18
  • 발행 : 2010.03.30
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this paper is to apply a new proposed accuracy indicator to quantify the true and false modes for Eigensystem Realization Algorithm using output-based responses. First, a discrete mass-spring system and a simply supported continuous beam were modelled using finite element method. Then responses are simulated under random excitation. Natural Excitation Technique using only response measurements is applied to compute the impulse responses. Eigensystem Realization Algorithm is employed to identify the modal parameters on the simulated responses. A new accuracy indicator, Normalized Occurrence Number-NON, is developed to quantitatively partition the realized modes into true and false modes so that the false portions can be disregarded. Numerical simulation demonstrates that the new accuracy indicator can determine the true system modes accurately.

키워드

참고문헌

  1. Andersen, P. (1997), Identification of Civil Engineering Structures Using Vector ARMA Models, Ph.D Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Denmark.
  2. Bodeux, J.B. and Golinval, J.C. (2003), "Modal identification and damage detection using datadriven stochastic subspace and ARMA methods", Mech. Syst. Signal Pr., 17(1), 83-89. https://doi.org/10.1006/mssp.2002.1543
  3. Desforges, M.J., Cooper, J.E. and Wright, J.R. (1995), "Spectral and modal parameter estimation from outputonly measurements", Mech. Syst. Signal Pr., 9(2), 169-186. https://doi.org/10.1006/mssp.1995.0014
  4. Gibson, J.D. and Melsa, J.L. (1975), Introduction to Nonparametric Detection with Applications. Academic Press, New York.
  5. Ibrahim, S.R. and Mickulcik, E. (1977), "A method for the direct identification of vibration parameters from the free responses", Shock Vib., 47(4), 183-198.
  6. James, G.H., Carne, T.G. and Lauffer, J.P. (1993), The Natural Excitation Technique for Modal Parameter Extraction from Operating Wind Turbines. SAND92-1666, UC-261, Sandia National Laboratories.
  7. Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization algorithm for modal parameter identification and model reduction", J. Guid. Control Dynam., 8(5), 620-627. https://doi.org/10.2514/3.20031
  8. Klema, V.C. and Laub, A.J. (1980), "The singular value decomposition: Its computation and some applications", IEEE T. Automat. Contr., AC-25, 164-176.
  9. Mohanty, P. and Rixen, D.J. (2006), "Modified ERA method for operational modal analysis in the presence of harmonic excitations", Mech. Syst. Signal Pr., 20, 114-130. https://doi.org/10.1016/j.ymssp.2004.06.010
  10. Pandit, S.M. and Wu, S.M. (1983), Time Series and System Analysis with Applications, Wiley, New York.
  11. Pappa, R.S., Elliott, K.B. and Schenk, A. (1993), "Consistent-mode indicator for the eigensystem realization algorithm", J. Guid. Control Dynam., 16(5), 852-858. https://doi.org/10.2514/3.21092
  12. Van Oerschee, P. and De Moor, B. (1996), "Subspace identification for linear systems: theory, implementation and applications", Dordrecht: Kluwer Academic Publishers.
  13. Wang, S. and Li, H. (2005), "Modal identification of offshore platforms using statistical method based on ERA", China Ocean Eng., 19(2), 175-184.
  14. Zhang, Y., Zhang, Z., Xu, X. and Hua, H. (2005), "Modal parameter identification using response data only", J. Sound Vib., 282, 367-380. https://doi.org/10.1016/j.jsv.2004.02.012

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